Automatic Audio-based Screening System for Alzheimer’s Disease Detection

Alzheimer’s disease (AD) and other types of dementia have become a public health priority worldwide. To lessen the burden of AD diagnosis, an automatic screening system that can be deployed in large-scale and cost-efficient screening methods will be needed. This paper presents a speech assessment system for cognitive impairment detection, detecting whether elders have AD or suffer from mild cognitive impairment (MCI) based on their audio recordings taken from neuropsychological tests. The audio waveform first is transformed to Mel-spectrogram and done the downsampling. With the combination of Transformer and convolutional neural network (CNN) architecture, we can do the feature extraction and get a better representation for the classifier. We conducted experiments on 120 subjects with a balanced distribution of ordinary aging, MCI, and AD patients to validate our study. Our experiments achieve an accuracy of 91% and 79% for classifying groups of AD and MCI from ordinary aging people, respectively.